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<body>
<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################

# load packages
library(dplyr)   # CRAN v1.0.5
library(tidyr)   # CRAN v1.1.3
library(MuMIn)   # CRAN v1.43.17
library(stringr) # CRAN v1.4.0
library(cowplot) # CRAN v1.1.1
library(car)     # CRAN v3.0-10
library(ggplot2) # CRAN v3.3.3

here::here()
</code></pre>

<pre><code>## [1] &quot;/Users/smueller/Dropbox/papers/ireland_incumbency_gaa&quot;
</code></pre>

<pre><code class="r">## alternatively, set working directory manually
# setwd(&quot;...&quot;)

source(&quot;function_theme_base.R&quot;)

## parts of this code are adjusted from
## https://dcosme.github.io/2019/02/26/specification-curve-example/

## load dataset with elections
dat_general_raw &lt;- readRDS(&quot;data_elections_general_full.rds&quot;) 


## only keep: incumbents whose county either lost or won a game
## = all &quot;treated&quot; incumbents
## the coefficienf for winner_loser_before_num will show 
## whether an incumbent increases her vote share when the county team
## won shortly before the election (winner_loser_before_num = 1)

dat_general_incumbents_treated &lt;- dat_general_raw %&gt;% 
    filter(incumbent == &quot;Incumbent (Candidate elected in t-1)&quot;) %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) %&gt;% 
    mutate(decade = factor(paste0(substr(election_id, 1, 3), &quot;0&quot;))) %&gt;% 
    mutate(winner_loser_before = as.character(winner_loser_before)) %&gt;% 
    mutate(winner_loser_before_num = ifelse(winner_loser_before == &quot;Win&quot;, 1, 0))


#                # create a dummy for strongholds
dat_general_incumbents_treated &lt;- dat_general_incumbents_treated %&gt;% 
    mutate(stronghold_dummy = ifelse(str_detect(stronghold_win_loss, &quot;Stronghold&quot;), 
                                     &quot;Stronghold&quot;, &quot;Non-stronghold&quot;))


## set na.action for dredge
options(na.action = &quot;na.fail&quot;)


## select only the variables included in the regression models 
## and omit missing values to have the same N across all models

dat_general_incumbents_treated_no_na &lt;- dat_general_incumbents_treated %&gt;% 
    ungroup() %&gt;% 
    select(diff_share, county_gaa, 
           winner_loser_before_num, turnout, 
           election_id, sport, margin,
           party_recoded, stronghold_dummy) %&gt;% 
    na.omit() 


summary(dat_general_incumbents_treated_no_na$winner_loser_before_num)
</code></pre>

<pre><code>##    Min. 1st Qu.  Median 
##  0.0000  0.0000  1.0000 
##    Mean 3rd Qu.    Max. 
##  0.6147  1.0000  1.0000
</code></pre>

<pre><code class="r">## number of complete observations in this subset
nrow(dat_general_incumbents_treated_no_na)
</code></pre>

<pre><code>## [1] 218
</code></pre>

<pre><code class="r">## run full model
full_model_general &lt;- lm(diff_share ~ 
                             winner_loser_before_num +
                             county_gaa +
                             turnout +
                             margin +
                             election_id +
                             sport +
                             party_recoded +
                             stronghold_dummy,
                         data = dat_general_incumbents_treated_no_na)


## run all possible nested models but only keep thos models that include
## the treatment variable (winner_loser_before)

all_models_general &lt;-  dredge(full_model_general) 
</code></pre>

<pre><code>## Fixed term is &quot;(Intercept)&quot;
</code></pre>

<pre><code class="r">nrow(all_models_general)
</code></pre>

<pre><code>## [1] 256
</code></pre>

<pre><code class="r">## filter only models that include the main independent variable of interest (winner/loser)
all_models_general &lt;- filter(all_models_general, !is.na(winner_loser_before_num))

nrow(all_models_general)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## get coefficients and standard errors for all estimates in each model
all_models_general_coefs &lt;- coefTable(all_models_general)

length(all_models_general_coefs)
</code></pre>

<pre><code>## [1] 256
</code></pre>

<pre><code class="r">## transform list of coefficients and standard errors to a data frame
df_all_models_general_coefs &lt;- do.call(rbind.data.frame, all_models_general_coefs)

## get coefficient name from rownames
df_all_models_general_coefs$coefficient &lt;- rownames(df_all_models_general_coefs)


## filter only those coefficients relating to winner_loser_before_num
## because these values are needed to extract the standard errors of the coefficients
df_coefs_se_winner_loser_before &lt;- filter(df_all_models_general_coefs, 
                                          str_detect(coefficient, &quot;winner_loser&quot;)) 

#                # create variable for merging below
df_coefs_se_winner_loser_before &lt;- df_coefs_se_winner_loser_before %&gt;% 
    mutate(winner_loser_before_num = Estimate) 

## merge standard errors for the independent variable of interest
## by using the point estimates of winner_loser_before_num
## as the merging variable (not elegant, but not possible to do it differently)

## check that estimate colums are unique 
unique_valid_models &lt;- nrow(filter(all_models_general, !is.na(winner_loser_before_num)))
unique_valid_models
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">unique_coefs &lt;- length(unique(all_models_general$winner_loser_before_num))
unique_coefs
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">unique_coefs_se &lt;- length(unique(df_coefs_se_winner_loser_before$winner_loser_before_num))
unique_coefs_se
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## this code works only if the number of unique 
## coefficients is the same in both dataframes!
## Be very careful if you adjust this code for your own work.

## stop the code if the numbers differ
stopifnot(unique_coefs_se == unique_coefs)

all_models_winner_loser_before &lt;- left_join(all_models_general,
                                            df_coefs_se_winner_loser_before,
                                            by = c(&quot;winner_loser_before_num&quot;))


nrow(all_models_winner_loser_before)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">nrow(all_models_general)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## get variables included in the model
variable.names &lt;-  names(dat_general_incumbents_treated_no_na)[-1]


## prepare data for plot
plot_data_general &lt;-  all_models_winner_loser_before %&gt;%
    filter(!is.na(Estimate)) %&gt;% ## remove models without coefficient for winner_loser_before_num
    arrange(Estimate) %&gt;% ## arrange estimates in ascending order
    mutate(specification = row_number()) 

nrow(plot_data_general) ## number of models
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## check how many coefficients are positive/negative and how many are
## statistically significant

plot_data_general_check &lt;- plot_data_general %&gt;% 
    mutate(significant_95 = (Estimate - 1.96 * `Std. Error`) &gt; 0 |
               (Estimate + 1.96 * `Std. Error`) &lt; 0) %&gt;% 
    mutate(direction = ifelse(Estimate &gt; 0, &quot;Positive&quot;,
                              ifelse(Estimate &lt; 0, &quot;Negative&quot;, &quot;0&quot;)))


table(plot_data_general_check$direction,
      plot_data_general_check$significant_95)
</code></pre>

<pre><code>##           
##            FALSE TRUE
##   Negative    91    2
##   Positive    35    0
</code></pre>

<pre><code class="r">plot_top_general &lt;- plot_data_general %&gt;%
    ggplot(aes(x = specification, 
               y = Estimate)) + 
    geom_linerange(aes(ymin = Estimate - 1.96 * `Std. Error`,
                       ymax = Estimate + 1.96 * `Std. Error`),
                   colour = &quot;grey50&quot;) +
    geom_point(size = 1) +
    geom_hline(yintercept = 0, linetype = &quot;dashed&quot;, color = &quot;red&quot;) + ## dashed line for null model AIC
    labs(x = &quot;&quot;, y = &quot;Coefficient estimate for Winner&quot;)
plot_top_general
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">## note: warning will appear because not all models contain all variables 
## (therefore, the attributes are not identical and will be dropped)

plot_data_general_long &lt;-  plot_data_general %&gt;%
    gather(variable, value, eval(variable.names)) %&gt;% 
    mutate(value = ifelse(!is.na(value), &quot;|&quot;, &quot;&quot;)) ## plot a line if included in the model
</code></pre>

<pre><code>## Warning: attributes are not identical across measure variables;
## they will be dropped
</code></pre>

<pre><code class="r">## remove winner_loser_before_num because this variable is 
## included in all models and this coefficient is displayed in the graph

plot_data_general_long &lt;- plot_data_general_long %&gt;% 
    filter(variable != &quot;winner_loser_before_num&quot;)


table(plot_data_general_long$variable)
</code></pre>

<pre><code>## 
##       county_gaa 
##              128 
##      election_id 
##              128 
##           margin 
##              128 
##    party_recoded 
##              128 
##            sport 
##              128 
## stronghold_dummy 
##              128 
##          turnout 
##              128
</code></pre>

<pre><code class="r">recode_vars &lt;- c(&quot;&#39;county_gaa&#39;=&#39;County team&#39;;
                 &#39;difference&#39;=&#39;Days after match&#39;;
                 &#39;election_id&#39;=&#39;Election&#39;;
                 &#39;margin&#39;=&#39;Margin of result&#39;;
                 &#39;party_recoded&#39;=&#39;Party&#39;;
                 &#39;sport&#39;=&#39;Sport&#39;;
                 &#39;stronghold_dummy&#39;=&#39;Stronghold&#39;;
                 &#39;turnout&#39;=&#39;Turnout&#39;&quot;)



plot_data_general_long &lt;- plot_data_general_long %&gt;% 
    mutate(variable = car::recode(variable, recode_vars))


plot_bottom_general &lt;-   ggplot(plot_data_general_long,
                                aes(specification, variable)) +
    geom_text(aes(label = value), alpha = 1) +
    labs(x = &quot;Specification number&quot;, y = &quot;Variables&quot;)

plot_bottom_general
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">## combine both plots
plot_grid(plot_top_general, plot_bottom_general, ncol = 1, align = &quot;v&quot;,
          rel_heights = c(0.6, 0.4))
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a15.pdf&quot;,
       width = 9, height = 5.5)



## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 09:57:41 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
</code></pre>

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